Spatial Assessment of Territorial Resilience to Floods Using Comprehensive Indicators: Application to Greater Papeete (French Polynesia)

International audienceFlash floods and coastal flooding are more and more frequent and damaging in the context of climate change. In addition, the concentration of the population in urban areas contributes to increasing flood risk in these areas. Furthermore, not all territories are at the same level in their risk and resilience management approaches. Regarding this, the French overseas territories have been identified as particularly vulnerable to flood risk. This is the case for Tahiti, the main island of French Polynesia where the capital is located. It is a dense urban area subject to coastal and river flooding hazards, largely exacerbated by the physical environment. Our goal is to propose a method to assess flood resilience in Tahiti. We developed an indicator‐based method and used GIS to produce and represent a spatial analysis of territorial resilience. We developed a list of comprehensive spatial indicators that take into account three main dimensions: a structural dimension (e.g., building resilience), an organisational dimension (e.g., the resilience of actions during crisis) and a socio‐economic dimension (e.g., human economic capital). The final objective of this research is to design decision‐making tools for territorial stakeholders to help them in long‐term reflection and collaboration

Context normalization: A new approach for the stability and improvement of neural network performance

International audienc

Enhancing public understanding of extreme weather events in a changing climate through ClimaMeter

ClimaMeter is a real-time platform designed to provide rapid, science-based assessments of extreme weather events and their links to climate change. ClimaMeter's methodology relies on identifying large-scale atmospheric circulation patterns and comparing them to historical data, analysing how the intensity of extreme weather events have changed because of anthropogenic climate change or natural climate variability. By leveraging historical climate data, machine learning, and real-time weather observations, ClimaMeter delivers near-instantaneous attribution results, enabling informed decision-making in a time when media cycles and public attention are brief. This speed is crucial for climate action, as it helps policymakers, emergency responders, and the public understand the role of climate change in specific extreme events and take timely, effective measures. This allows for quicker, data-driven responses to disasters, such as the 2023 French heatwave and Storm Poly, by informing disaster response, infrastructure planning, and resilience-building efforts. ClimaMeter also plays a key role in countering climate change misinformation, offering clear, evidence-based explanations to the public and media. By bridging the gap between scientific research and policy applications, ClimaMeter supports climate action, promotes public awareness, and aids in the development of adaptation and mitigation strategies to address the growing risks posed by climate change

Timescale Matters: Finer Temporal Resolution Influences Driver Contributions to Global Soil Respiration

International audienceABSTRACT Understanding the dynamics of soil respiration ( R s ) and its environmental drivers is crucial for accurately modeling terrestrial carbon fluxes. However, current methodologies often lead to divergent estimates and rely on annual predictions that may overlook critical interactions occurring at seasonal scales. A critical knowledge gap lies in understanding how temporal resolution affects both R s predictions and their environmental drivers. Here, we employ deep learning models to predict global R s at monthly (MRM) and annual (ARM) scales from 1982 to 2018. We then consider three main drivers potentially affecting R s , including temperature, precipitation, and a vegetation proxy (leaf area index; LAI). Our models demonstrate strong predictive capabilities with global R s estimation of 79.4 ± 5.7 Pg C year −1 for the MRM and 78.3 ± 7.5 Pg C year −1 for ARM (mean ± SD). While the difference in global estimations between both models is small, there are notable disparities in the spatial contribution of dominant drivers. The MRM highlights an influence of both temperature and LAI, while the ARM emphasizes a dominant role of precipitation. These findings underscore the critical role of temporal resolution in capturing seasonal variations and identifying key R s ‐environment relationships that annual models may obscure. High temporal resolution R s predictions, such as those provided by the MRM, are essential for capturing nuanced seasonal interactions between R s and its drivers, refining carbon flux models, detecting critical seasonal thresholds, and enhancing the reliability of future Earth system predictions. This work highlights the need for further research into monthly and seasonal R s variations, as well as higher timescale resolutions, to advance our understanding of ecosystem carbon dynamics in a rapidly changing climate

A versatile integrated protocol to extract organic balms from archaeological linen: A new way to provide reliable radiocarbon dating for contaminated textile

International audienceRadiocarbon dating of archaeological textiles can be particularly challenging when exogenous organic balms were deposited on their surface, as these organic mixtures can sometimes contain radiocarbon-depleted materials such as fossil bitumen. This is a key issue for radiocarbon dating of linen fragments used in the wrapping of Egyptian mummies, as bitumen has been repeatedly identified in several contexts. Radiocarbon dating of contaminated fragments can be facilitated by an analytical approach involving textile surface analysis by ATR-IR (Attenuated Total Reflection – Infrared Spectroscopy) to diagnose the state of contamination observed on each fragment, followed by a three-step organic extraction to remove all chemical families identified. This study was organized in two parts. First, mock-up samples were prepared, made of modern linen and experimental balms, to develop an integrated methodology based first on ATR-IR diagnosis for the state of contamination of textile, then on a solvent extraction to remove organic contaminants. The solvent extraction was monitored by ATR-IR and radiocarbon dating to control the complete removal of fossil compounds. The extraction protocol chosen is a three-step procedure (3x CHCl3, then 3x hexane, then 3x MeOH), which can be tunable depending on the state and the nature of organic contamination on the textile. This new integrated methodology, which can be used in a versatile way to ensure reliable radiocarbon dating of linen archaeological textiles, was then applied to samples collected from an Egyptian child mummy conserved in Musée des Confluences, embalmed using a process that included the use of fossil bitumen. Thanks to this new protocol, we were able to date this mummy to the Late Period of Egypt

Ensemble random forest for tropical cyclone tracking

Even though tropical cyclones (TCs) are well documented during the intense part of their lifecycle until they start to evanesce, many physical and statistical properties governing them are not well captured by gridded reanalysis or simulated by earth system models. Thus, the tracking of TCs remains a matter of interest for the investigation of observed and simulated tropical cyclones. Two types of cyclone tracking schemes are available. On the one hand, there are trackers that rely on physical and dynamical properties of the TCs and user-prescribed thresholds, which make them rigid. They need numerous variables that are not always available in the models. On the other hand, there are trackers leaning on deep learning which, by nature, need large amounts of data and computing power. Besides, given the number of physical variables needed for the tracking, they can be prone to overfitting, which hinders their transferability to climate models. In this study, the ability of a Random Forest (RF) approach to track TCs with a limited number of aggregated variables is explored. Hence, the tracking is considered as a binary supervised classification problem of TC-free (zero) and TC (one) situations. Our analysis focuses on the Eastern North Pacific and North Atlantic basins, for which, respectively, 514 and 431 observed tropical cyclone track records are available from the IBTrACS database during the 1980-2021 period. For each 6-hourly time step, RF associates TC occurrence or absence (1 or 0) to atmospheric situations described by predictors extracted from the ERA5 reanalysis. Then situations with TC occurrences are joined for reconstructing TC trajectories. Results show the ability and performance of this method for tracking tropical cyclones over both basins, and good temporal and spatial generalization as well. RF has a similar TC detection rate as trackers based on TCs' properties and significantly lower false alarm rate. RF allows us to detect TC situations for a range of predictor combinations, which brings more flexibility than threshold based trackers. Last but not least, this study shed light on the most relevant variables allowing to detect tropical cyclone

Methane emissions from the Nord Stream subsea pipeline leaks

International audienceThe amount of methane released to the atmosphere from the Nord Stream subsea pipeline leaks remains uncertain, as reflected in a wide range of estimates 1-18 . A lack of information regarding the temporal variation in atmospheric emissions has made it challenging to reconcile pipeline volumetric (bottom-up) estimates 1-8 with measurement-based (top-down) estimates 8-18 . Here we simulate pipeline rupture emission rates and integrate these with methane dissolution and sea-surface outgassing estimates 9,10 to model the evolution of atmospheric emissions from the leaks. We verify our modelled atmospheric emissions by comparing them with top-down point-in-time emission-rate estimates and cumulative emission estimates derived from airborne 11 , satellite 8,12-14 and tall tower data. We obtain consistency between our modelled atmospheric emissions and top-down estimates and find that 465 ± 20 thousand metric tons of methane were emitted to the atmosphere. Although, to our knowledge, this represents the largest recorded amount of methane released from a single transient event, it is equivalent to 0.1% of anthropogenic methane emissions for 2022. The impact of the leaks on the global atmospheric methane budget brings into focus the numerous other anthropogenic methane sources that require mitigation globally. Our analysis demonstrates that diverse, complementary measurement approaches are needed to quantify methane emissions in support of the Global Methane Pledge 19 . Subsea natural gas leaks from pipeline ruptures and well blowouts can emit large quantities of methane (CH 4 ) to the ocean and atmosphere. Prominent examples are the 22/4b well blowout 20,21 , the Deepwater Horizon oil disaster 22,23 and the Elgin rig blowout 24 . The Nord Stream 1 and 2 twin pipeline systems (NS1 and NS2) are a network of offshore pipelines underlying the Baltic Sea that connect the Russian natural gas supply with Europe 25-27 . On 26 September 2022, damage to both NS1 and NS2 occurred in a series of underwater explosions, resulting in the leakage of natural gas (Fig. 1). The first explosion occurred at 00:03 UTC southeast of the Danish Island of Bornholm in the Bornholm Basin, rupturing pipeline A of the twin NS2 pipeline system (NS2A) at approximately 70 m depth 28-30 . This explosion destroyed approximately 10 m of the pipeline 31 . At 17:03 UTC, multiple explosions to the northeast of Bornholm ruptured both NS1 pipelines (NS1A and NS1B) at depths of approximately 75 m (ref. 30) and caused a smaller partial rupture in the NS2A pipeline to the north of the previous NS2A leak 31,32 . These explosions destroyed 200-300 m of the NS1A and NS1B pipelines 31 . The NS2B pipeline remained undamaged 33 .Although not operational at the time, both NS1 and NS2 pipeline systems were filled with pressurized natural gas 1-5 . The natural gas released from the ruptured pipelines was predominantly CH 4 along with small amounts of ethane, nitrogen and other hydrocarbons 34 . The emitted gas was seen bubbling through the sea surface above the four rupture sites, creating bulging mounds of foamy seawater of up</div

Evaluation of autologous venous allograft for lower limb in the treatment of critical limb ischemia. The REVATEC (REVAscularisation par greffons veineux bioproTEC) study.

International audienceBackgroundCritical limb threatening ischemia (CLTI) requires revascularization whenever it is possible. The great saphenous vein represents the surgical conduit of choice. However, it is not always available, in particular in multi-operated patients. In such cases, alternative efficient biological conduits are needed but data remains limited. This study aims at evaluating the performance of cold stored venous allografts provided by Bioprotec® society.MethodsProspective multi-center cohort. The primary endpoint was limb salvage rate at 1 year following revascularization with cold stored venous allografts. Follow-up based on clinical examination and duplex-scan. Uni- and multivariate analyses were performed to analyze predictive factors of endpoints.ResultsOverall, 39 patients (40 limbs) were included between 2018 and 2021. Patients had a median of 2 [0–6] revascularizations prior to inclusion. A total of 97 grafts were used (median of 3 [1–4] grafts per procedure). In the postoperative period (30 days) no death and 4 major amputations were noted. The median length of follow-up was 13.4 [0.7–31.1] months. The 6-months, 1-year and 2-year freedom from major amputation rates were 79% [95% CI: 68–93], 75% (95% CI 62%–91%) and 68% [95% CI: 51–90], respectively. The 6 months, 1-year and 2-year survival rates were 95% [88–100], 83% [95% CI: 71–98] and 79% [95% CI: 65–96], respectively. Primary patency rates were 77% [95% CI: 64–91] at 6 months, and 47% [95% CI: 32–70] at one and 2 years. Secondary patency rates were 82% [95% CI: 70–95] at 6 months and 50% [95% CI: 34–73] at one and 2 years. The analysis identified the number of previous revascularizations as a significant risk factor for graft patency (Hazard Ratio: 1.59; 95% Confidence Interval: 1.13–2.24).ConclusionRevascularization of CLTI patients with previous failed interventions is highly challenging. The use of cold stored venous allograft showed encouraging limb salvage rate despite modest patency rates and thus may represent an alternative to other substitute in some selected cases. More studies are necessary to identify the potential of CSVA in CLTI patients

Arctic soil carbon insulation averts large spring cooling from surface–atmosphere feedbacks

International audienceThe insulative properties of soil organic carbon (SOC) and surface organic layers (moss, lichens, litter) regulate surface–atmosphere energy exchanges in the Arctic through a coupling with soil temperatures. However, a physical description of this process is lacking in many climate models, potentially biasing their high-latitude climate predictions. Using a coupled surface–atmosphere model, we identified a strong feedback loop between soil insulation, surface air temperature, and snowfall. Without insulation, the latent heat needed for soil ice thawing leads to a late spring and summer cold bias in surface air temperature (above 2 °C) over Arctic regions. The integration of soil insulation eliminates this bias and significantly improves the simulation of permafrost dynamics. Our findings, including the potential consequences of large perturbations (e.g., fires), highlight the importance of combining soil water freezing with a physical representation of SOC and surface organic layer insulation in Earth system models, to improve Arctic climate predictions